This script examines the discharge in Neversink Cr.
This script uses the USGS data retrieval package to download data fro Neversink Cr. This is more up to date than the excel sheet.
Install libraries.
These are the libraries that are used in the script. These are installed only once and then can be commented out so they do not run in future execuations of the script.
# https://github.com/USGS-R/dataRetrieval
# general R packages to install if you dont have them
# remove the "#" if you need to run the installations
# install.packages("devtools") # essential in installing other thigs
# install.packages("tidyverse") # installs a lot of things and ggplot
# install.packages("scales") # allows great scale formatting on ggplot
# install.packages("lubridate") # makes working with dates easier
# install.packages("janitor") # clean names of columns and other things
# install.packages("readxl") # read in excel files
# install.packages("plotly") # interactive plots
# install.packages("skimr")
# install.packages("patchwork") # multiple plots
# specific to this module
# install.packages("dataRetrieval") # USGS Data Retreiveal Method
Load Libraries
This code loads the libraries for working with the data
# load these every time
library(tidyverse) # the tidyverse library with many sub libraries
library(scales) # working with ggplot scales
library(lubridate) # working with dates
library(janitor) # cleans up excel imports
library(readxl) # read in excel files
library(skimr) # does summary stats and other things
library(plotly) # makes interactive plots
library(patchwork) # combines many plots together
# # load the USGS package
library(dataRetrieval) # utilities for downloading and working with USGS data
Read in USGS Data
This allows you to read in the daily mean data for the USGS site.
Note Enter the following information and you can change this to get different sites or time ranges:
site_no: this is the site that you want to import
par_code: these are the paramter code numbers - 00060 is discharge
You can retreive other variables using:
c(“00060”, “00065”)
00010 water temp
00060 discharge
00065 gage height
00095 specific conductivity if available
start.date: date to start on
end.date: date to end on
# The site is Neversink and the site number is 01435000
# USGS 01435000 NEVERSINK RIVER NEAR CLARYVILLE NY
# enter these variables
site_no <- "01435000"
par_code <- "00060"
start.date <- "1800-01-01"
end.date <- "2050-01-01"
neversink_daily.df <- readNWISdv(siteNumbers = site_no,
parameterCd = par_code,
startDate = start.date,
endDate = end.date)
# rename the columns
neversink_daily.df <- renameNWISColumns(neversink_daily.df)
Rename columns
This just renames the columns to lower case and variable names with units
neversink_daily.df <- neversink_daily.df %>%
select(agency_cd,
site_no,
date = Date,
discharge_cfs = Flow)
Create a month variable
The module would like to look at flows during differnet months. This creates a month column that can then be used to filter out and plot only the respective months.
neversink_daily.df <- neversink_daily.df %>%
mutate(month = month(date))
Plot all discharge data
This is a plot of all of the data that is on the USGS website for daily mean values.
# saves the plot as q.plot so can be used later on
q.plot <- neversink_daily.df %>% # use this dataframe
ggplot(aes(date, discharge_cfs)) + # plot discharge_cfs versus date
geom_point(size=0.1) + # add points that are small
geom_line() # add a line on top of points
q.plot # call the plot to display

Plot February data
This creates a plot of just February data. It can do this using pipes rather than creating subdataframes which is a key advantage of tidyverse.
# saves as the feb.plot using the neversink_daily.df data
feb.plot <- neversink_daily.df %>%
filter(month == 2) %>% # this filters out only February
ggplot(aes(date, discharge_cfs)) + # this plots discharge versus date
geom_point(size = 0.1) + # adds small points
geom_line() + # adds a line
labs(y="Mean Monthly Discharge m^3/sec", x="Date") # adds clean axes labels
feb.plot

NA
Interactive plot of feb data
This is an interactive plot of the february data. You can zoom in and out and select particular pints to see the values.
ggplotly(feb.plot)
August data
Plot of August data only using eh above formate. The only thing to change is the value for the month variable. This could be improved by showing the monthly mean of data in the same format below adding a a stat_summary command to show monthly mean as well.
aug.plot <- neversink_daily.df %>%
filter(month == 8) %>%
ggplot(aes(date, discharge_cfs)) +
geom_point() +
geom_line() +
labs(y="Mean Monthly Discharge m^3/sec", x="Date")
aug.plot

Compare the February and August plots
We can use patchwork to put both plots on the same page to compare them
feb_aug.plot <- feb.plot +
aug.plot +
plot_layout(ncol = 2)
feb_aug.plot

Add trend lines of discharge over time
Because the data is over such a wide range I have zoomed in. There are two ways to zoom - the scale_x_continuous(limits = c(0,300)) will delete data that is not in this range. The coord_cartesian(ylim=c(0,300)) will zoom in without deleting data.
feb_aug.plot <- feb.plot +
geom_smooth(method="lm") +
coord_cartesian(ylim = c(0,300)) +
aug.plot + geom_smooth(method="lm") +
coord_cartesian(ylim = c(0,300)) +
plot_layout(ncol = 2)
feb_aug.plot

Regression statistics of February data
feb.model <- neversink_daily.df %>%
filter(month == 2) %>%
lm(discharge_cfs ~ date, data=.)
summary(feb.model)
Call:
lm(formula = discharge_cfs ~ date, data = .)
Residuals:
Min 1Q Median 3Q Max
-129.4 -86.5 -55.4 0.3 5925.8
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.585e+02 5.282e+00 30.007 <2e-16 ***
date 1.393e-03 5.713e-04 2.439 0.0148 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 233.5 on 2258 degrees of freedom
Multiple R-squared: 0.002627, Adjusted R-squared: 0.002186
F-statistic: 5.948 on 1 and 2258 DF, p-value: 0.01481
Regression statistics of August data
aug.model <- neversink_daily.df %>%
filter(month == 8) %>%
lm(discharge_cfs ~ date, data=.)
summary(aug.model)
Call:
lm(formula = discharge_cfs ~ date, data = .)
Residuals:
Min 1Q Median 3Q Max
-91.5 -61.5 -36.9 -2.6 7104.8
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.495e+01 4.794e+00 17.720 < 2e-16 ***
date 1.987e-03 5.152e-04 3.856 0.000118 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 220.2 on 2477 degrees of freedom
Multiple R-squared: 0.005966, Adjusted R-squared: 0.005564
F-statistic: 14.87 on 1 and 2477 DF, p-value: 0.0001184
---
title: "Eddie module stream discharge from handout"
output: html_notebook
editor_options: 
  chunk_output_type: inline
---

# This script examines the discharge in Neversink Cr.      
This script uses the USGS data retrieval package to download data fro Neversink Cr. This is more up to date than the excel sheet.

## Install libraries.   
These are the libraries that are used in the script. These are installed only once and then can be commented out so they do not run in future execuations of the script.
```{r install packages, message=TRUE, warning=TRUE}
# https://github.com/USGS-R/dataRetrieval

# general R packages to install if you dont have them
# remove the "#" if you need to run the installations

# install.packages("devtools") # essential in installing other thigs
# install.packages("tidyverse") # installs a lot of things and ggplot
# install.packages("scales") # allows great scale formatting on ggplot
# install.packages("lubridate") # makes working with dates easier
# install.packages("janitor") # clean names of columns and other things
# install.packages("readxl") # read in excel files
# install.packages("plotly") # interactive plots
# install.packages("skimr")
# install.packages("patchwork") # multiple plots

# specific to this module
# install.packages("dataRetrieval") # USGS Data Retreiveal Method
```


## Load Libraries    
This code loads the libraries for working with the data
```{r laod libraries, message=TRUE, warning=TRUE}
# load these every time  
library(tidyverse) # the tidyverse library with many sub libraries
library(scales) # working with ggplot scales 
library(lubridate) # working with dates
library(janitor) # cleans up excel imports
library(readxl) # read in excel files
library(skimr) # does summary stats and other things
library(plotly) # makes interactive plots
library(patchwork) # combines many plots together

# # load the USGS package
library(dataRetrieval) # utilities for downloading and working with USGS data
```

## Source of Neversink data
This is the data that is in the excel sheet that is used in this script.        
https://waterdata.usgs.gov/ny/nwis/uv/?site_no=01435000&PARAmeter_cd=00065,00060,63160
 
This is all of the data that is available for the site.         
https://waterdata.usgs.gov/nwis/inventory/?site_no=01435000&agency_cd=USGS


## Read in USGS Data
This allows you to read in the daily mean data for the USGS site.      
Note Enter the following information and you can change this to get different sites or time ranges:        
site_no: this is the site that you want to import     
par_code:  these are the paramter code numbers - 00060 is discharge     
           You can retreive other variables using:     
           c("00060", "00065")    
           00010 water temp    
           00060 discharge     
           00065 gage height      
           00095 specific conductivity if available
           
start.date: date to start on      
end.date:   date to end on     

```{r read in file}

# The site is Neversink and the site number is 01435000
# USGS 01435000 NEVERSINK RIVER NEAR CLARYVILLE NY

# enter these variables and they will be used in the next command
site_no <-          "01435000" # the site number from USGS
par_code <-         "00060" # the paramter code used for data to download
start.date <-      "1800-01-01" # start date - this is set to download all data that might be there
end.date <-        "2050-01-01" # end date - note will download up to 2050

# stores data in neversink_daily.df 
# these do not need to be changes as they are changed above
neversink_daily.df <- readNWISdv(siteNumbers = site_no,  
                                 # USGS read daily values with this site number
                        parameterCd = par_code, # parameters to download
                        startDate = start.date, # start date
                        endDate = end.date) # end date 
  
# rename the columns
neversink_daily.df <- renameNWISColumns(neversink_daily.df) # rename the columns to work better
```

# Rename columns
This just renames the columns to lower case and variable names with units   
```{r}
neversink_daily.df <- neversink_daily.df %>% # use this dataframe to manipulate it and save to same
# select selects which data to retain and can also rename variables
    select(agency_cd,  
         site_no, 
         date = Date, 
         discharge_cfs = Flow)  # renames Flow as discharge_cfs
```

# Create a month variable    
The module would like to look at flows during differnet months. This creates a month column that can then be used to filter out and plot only the respective months.    
```{r}
neversink_daily.df <- neversink_daily.df %>%
  mutate(month = month(date)) # creates a month column to work with
```

# Plot all discharge data
This is a plot of all of the data that is on the USGS website for daily mean values.
```{r}
# saves the plot as q.plot so can be used later on   
q.plot <- neversink_daily.df %>% # use this dataframe
  ggplot(aes(date, discharge_cfs)) + # plot discharge_cfs versus date
  geom_point(size=0.1) + # add points that are small
  geom_line() # add a line on top of points
q.plot # call the plot to display
```

# Plot February data 
This creates a plot of just February data. It can do this using pipes rather than creating subdataframes which is a key advantage of tidyverse. 
```{r}
# saves as the feb.plot using the neversink_daily.df data
feb.plot <- neversink_daily.df %>% 
  filter(month == 2) %>% # this filters out only February
  ggplot(aes(date, discharge_cfs)) + # this plots discharge versus date
  geom_point(size = 0.1) + # adds small points
  geom_line() + # adds a line
  labs(y="Mean Monthly Discharge m^3/sec", x="Date") # adds clean axes labels 
feb.plot
  
```

# Interactive plot of feb data
This is an interactive plot of the february data. You can zoom in and out and select particular pints to see the values.     
```{r}
ggplotly(feb.plot)
```


# August data
Plot of August data only using eh above formate. The only thing to change is the value for the month variable. This could be improved by showing the monthly mean of data in the same format below adding a a stat_summary command to show monthly mean as well.
```{r}
aug.plot <- neversink_daily.df %>%
  filter(month == 8) %>%
  ggplot(aes(date, discharge_cfs)) +
  geom_point() +
  geom_line() +
  labs(y="Mean Monthly Discharge m^3/sec", x="Date")
aug.plot
```

# Compare the February and August plots
We can use patchwork to put both plots on the same page to compare them
```{r}
feb_aug.plot <- feb.plot +
                aug.plot +
                plot_layout(ncol = 2)
feb_aug.plot
```

# Add trend lines of discharge over time
Because the data is over such a wide range I have zoomed in. There are two ways to zoom - the scale_x_continuous(limits = c(0,300)) will delete data that is not in this range. The coord_cartesian(ylim=c(0,300)) will zoom in without deleting data.
```{r}
feb_aug.plot <- feb.plot + # call the feb plot
                geom_smooth(method="lm") + # add a linear regression line
                        coord_cartesian(ylim = c(0,300)) + # zoom into 0 - 300 
                aug.plot + 
                      geom_smooth(method="lm") + # add a linear regression line
                      coord_cartesian(ylim = c(0,300)) + # zoom into 0 - 300 
                plot_layout(ncol = 2) # make the plot layout 2 columns
feb_aug.plot
```

# Regression statistics of February data
```{r}
feb.model <- neversink_daily.df %>% 
  filter(month == 2) %>%
  lm(discharge_cfs ~ date, data=.)
summary(feb.model)

```

# Regression statistics of August data
```{r}
aug.model <- neversink_daily.df %>% 
  filter(month == 8) %>%
  lm(discharge_cfs ~ date, data=.)
summary(aug.model)

```


